Assessment of Neuromuscular Fatigue from Muscle Synergies in Hand Poses

Surface electromyography (sEMG) is a common sensing modality for volitional control of robotic exoskeletons for the hand. However, neuromuscular fatigue can inhibit the reliability of sEMG-based control of robots, especially during prolonged use. Fatigue-awareness is needed for sEMG-based robotics to be viable for long-term motor augmentation, assistance, and rehabilitation. Prior works have explored time-frequency sEMG metrics indicative of fatigue, which are computationally expensive. Alternatively, sEMG analysis in the 'synergy'-domain can provide reliable, lower-dimensional metrics of neuromuscular fatigue from spatio-temporal patterns in muscle activation. Still, while much research effort has been expended towards synergy-domain sEMG analysis of lower limbs, work remains to establish the viability of synergy-domain sEMG analysis for fatigue-awareness during hand poses. In this manuscript, we present the assessment of neuromuscular fatigue via synergy-domain sEMG analysis in a pilot study with five healthy participants. We obtain time-frequency benchmarks and synergy-domain metrics of fatigue from sEMG data collected from the Flexor Digitorum Profundis, Flexor Pollicis Longus, and Extensor Digitorum Communis muscles during hand poses to illustrate that synergy-domain analysis is a reliable method to assess hand neuromuscular fatigue. We thereby show that synergy-domain sEMG analysis is viable for fatigue-aware hand exoskeleton control. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)